1. Technical Field
The present disclosure relates to the field of video monitoring and analysis.
2. Description of Related Art
In the field of video surveillance, monitor camera may be used, but the vast amount of video data collected from the monitor cameras often include only a few parts that are useful to user. The useful parts may present one or more unusual events, and people must look over or through all the video data to find them. Since this is not efficient use of manpower, data mining can be applied to video surveillance system in order to provide a better solution.
Data mining algorithms for detecting abnormal parts of the video are broadly based on principles of model analysis or cluster analysis. Cluster analysis is more reliable, since creating models for all “normal events” is hard in model analysis. However, cluster analysis may fail to detect a change in the background of the video as a significant or noteworthy moment. That makes the degree of accuracy obviously less as long as there is a dynamic background to the video footage.
Thus, the present disclosure is to provide an improved system and method for detecting abnormal events in a video regardless of whether the video under examination has a motionless or a dynamic background.